This environment implements a simple, single species logistic growth-based fishery.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Dynamics follow a logistic growth model.
Action Space The agent selects the amount of fish to harvest with respect to K. In this case, at interval of K/100ths: 0, K/100, K/50, 3K/100, K/25, …
Reward Function The agent is rewarded by the amount of fish harvested at a time step.
This environment implements a simple, single species logistic growth-based fishery.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Dynamics follow a logistic growth model.
Action Space The agent selects a quantity of fish to harvest with respect to K with a continuous interval.
Reward Function The agent is rewarded by the amount of fish harvested at a time step.
This environment implements a single species logistic growth-based fishery with a tipping point.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Dynamics follow a logistic growth model but below a population of 0.5K the population becomes much more likely to crash.
Action Space The agent selects a quantity of fish to harvest with respect to K with a continuous interval.
Reward Function The agent is rewarded by the amount of fish harvested at a time step.
This environment implements a single species logistic growth-based fishery with model error.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Dynamics follow a logistic growth model but with an r and K that are drawn from a normal distribution each episode.
Action Space The agent selects a quantity of fish to harvest with respect to K with a continuous interval.
Reward Function The agent is rewarded by the amount of fish harvested at a time step.
This environment implements a single species Allen model-based fishery.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Dynamics follow the Allen growth model.
Action Space The agent selects a quantity of fish to harvest with respect to K with a continuous interval.
Reward Function The agent is rewarded by the amount of fish harvested at a time step.
This environment implements a single species Beverton-Holt model-based fishery.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Dynamics follow the Beverton-Holt growth model.
Action Space The agent selects a quantity of fish to harvest with respect to K with a continuous interval.
Reward Function The agent is rewarded by the amount of fish harvested at a time step.
This environment implements a single species May model-based fishery.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Dynamics follow the May growth model.
Action Space The agent selects a quantity of fish to harvest with respect to K with a continuous interval.
Reward Function The agent is rewarded by the amount of fish harvested at a time step.
This environment implements a single species Myers model-based fishery.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Dynamics follow the Myers growth model.
Action Space The agent selects a quantity of fish to harvest with respect to K with a continuous interval.
Reward Function The agent is rewarded by the amount of fish harvested at a time step.
This environment implements a single species Ricker model-based fishery.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Dynamics follow the Ricker model.
Action Space The agent selects a quantity of fish to harvest with respect to K with a continuous interval.
Reward Function The agent is rewarded by the amount of fish harvested at a time step.
This environment implements a single species non-stationary Beverton-Holt model-based fishery.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Dynamics follow a Beverton-Holt growth model where r changes constantly over an episode by some amount, alpha.
Action Space The agent selects a quantity of fish to harvest with respect to K with a continuous interval.
Reward Function The agent is rewarded by the amount of fish harvested at a time step.
This environment implements a fishery where the transition dynamics vary each episode.
Observation Space The agent observes the fish population at that time step.
Model Dynamics Transition dynamics can follow May, Ricker, Allen, Beverton-Holt or Myers growth models. The dynamics model is randomly chosen every episode.
Action Space The agent selects a quantity of fish to harvest with respect to K with a continuous interval.
Reward Function The agent is rewarded by the amount of fish harvested at a time step.